Barbara Ann Graves1. 1. Capstone College of Nursing, The University of Alabama, Tuscaloosa, AL, USA.
Abstract
OBJECTIVE: The objective of this study was to determine the contribution of distance to hospitals with cardiac interventional services (CIS) to county age-adjusted myocardial infarction (MI) mortality rates (CAMR) in Alabama and Mississippi counties. METHODS: THE STUDY USED THREE DATA SOURCES: U.S. Census data, Centers for Disease Control and Prevention (CDC) mortality data, and American Hospital Association data. A geographical information system (GIS) was used to measure distance, providing an empirical measure of county access to CIS. Multiple regression analysis was conducted using measures of distance to CIS, county rural status, state, sex, poverty, education, race, and interaction as predictors of CAMR. RESULTS: Regression results indicate that the model significantly predicts CAMR, R(2) = .378, adjusted R(2) = .319, F = 6.321, p < .001. The model accounts for 31.9 percent of the variability. CONCLUSIONS: The results of this study do not lead to the conclusion that cardiac outcomes as measured by CAMR were sensitive to the geographic location of CIS. However, statistically significant interactions supported the sensitivity of CAMR to complex patterns and issues of rural status, poverty, education, and race.
OBJECTIVE: The objective of this study was to determine the contribution of distance to hospitals with cardiac interventional services (CIS) to county age-adjusted myocardial infarction (MI) mortality rates (CAMR) in Alabama and Mississippi counties. METHODS: THE STUDY USED THREE DATA SOURCES: U.S. Census data, Centers for Disease Control and Prevention (CDC) mortality data, and American Hospital Association data. A geographical information system (GIS) was used to measure distance, providing an empirical measure of county access to CIS. Multiple regression analysis was conducted using measures of distance to CIS, county rural status, state, sex, poverty, education, race, and interaction as predictors of CAMR. RESULTS: Regression results indicate that the model significantly predicts CAMR, R(2) = .378, adjusted R(2) = .319, F = 6.321, p < .001. The model accounts for 31.9 percent of the variability. CONCLUSIONS: The results of this study do not lead to the conclusion that cardiac outcomes as measured by CAMR were sensitive to the geographic location of CIS. However, statistically significant interactions supported the sensitivity of CAMR to complex patterns and issues of rural status, poverty, education, and race.
Entities:
Keywords:
cardiovascular disease; geographical information systems (GIS); healthcare access; healthcare disparities; myocardial infarction (MI) mortality
Authors: K Kuulasmaa; H Tunstall-Pedoe; A Dobson; S Fortmann; S Sans; H Tolonen; A Evans; M Ferrario; J Tuomilehto Journal: Lancet Date: 2000-02-26 Impact factor: 79.321
Authors: W J Rogers; J G Canto; C T Lambrew; A J Tiefenbrunn; B Kinkaid; D A Shoultz; P D Frederick; N Every Journal: J Am Coll Cardiol Date: 2000-12 Impact factor: 24.094
Authors: J G Canto; M G Shlipak; W J Rogers; J A Malmgren; P D Frederick; C T Lambrew; J P Ornato; H V Barron; C I Kiefe Journal: JAMA Date: 2000-06-28 Impact factor: 56.272
Authors: Luciano de Andrade; Vanessa Zanini; Adelia Portero Batilana; Elias Cesar Araujo de Carvalho; Ricardo Pietrobon; Oscar Kenji Nihei; Maria Dalva de Barros Carvalho Journal: PLoS One Date: 2013-03-19 Impact factor: 3.240